Michael J. Frank

2006-2008 University of Arizona, Tucson, AZ 
 2009- Brown University, Providence, RI 
computational models, basal ganglia, reinforcement learning, decision making
"Michael Frank"
Mean distance: 13.61 (cluster 23)
Cross-listing: Computational Biology Tree


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Randall C. O'Reilly grad student 1999-2004 CU Boulder
 (Dynamic dopamine modulation of striato-cortical circuits in cognition: Converging neuropsychological, psychopharmacological and computational studies.)
Tim Curran post-doc 2004-2005 CU Boulder


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Wasita Mahaphanit research assistant 2018- Brown
Thomas V. Wiecki grad student Brown
Alana Jaskir grad student 2018-
Shikhar Kumar grad student 2007-2009 University of Arizona
Nicholas T Franklin grad student 2011-2017 Brown
Harrison Ritz grad student 2016-2021 Brown
Meghan Elizabeth Gallo grad student 2020-2023 Brown
Guillaume Pagnier grad student 2019-2024
Ahmed A. Moustafa post-doc Rutgers, New Brunswick
Matt R. Nassar post-doc Brown
Anne GE Collins post-doc 2010- Brown
Michael X. Cohen post-doc 2008-2009 Aniversity of Arizona
James F. Cavanagh post-doc 2010-2013 Brown


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Kenneth J.D. Allen collaborator 2018-
BETA: Related publications


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Russin J, Pavlick E, Frank MJ. (2024) Human Curriculum Effects Emerge with In-Context Learning in Neural Networks. Arxiv
Pagnier GJ, Asaad WF, Frank MJ. (2024) Double dissociation of dopamine and subthalamic nucleus stimulation on effortful cost/benefit decision making. Current Biology : Cb
Culbreth AJ, Moran EK, Mahaphanit W, et al. (2023) A Transdiagnostic Study of Effort-Cost Decision-Making in Psychotic and Mood Disorders. Schizophrenia Bulletin
Jaskir A, Frank MJ. (2023) On the normative advantages of dopamine and striatal opponency for learning and choice. Elife. 12
Rac-Lubashevsky R, Cremer A, Collins A, et al. (2023) Neural index of reinforcement learning predicts improved stimulus-response retention under high working memory load. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience
Liu RG, Frank MJ. (2022) Hierarchical clustering optimizes the tradeoff between compositionality and expressivity of task structures for flexible reinforcement learning. Artificial Intelligence. 312
Barch DM, Boudewyn MA, Carter CC, et al. (2022) Cognitive [Computational] Neuroscience Test Reliability and Clinical Applications for Serious Mental Illness (CNTRaCS) Consortium: Progress and Future Directions. Current Topics in Behavioral Neurosciences
Fengler A, Bera K, Pedersen ML, et al. (2022) Beyond Drift Diffusion Models: Fitting a Broad Class of Decision and Reinforcement Learning Models with HDDM. Journal of Cognitive Neuroscience. 1-26
Provenza NR, Gelin LFF, Mahaphanit W, et al. (2021) Honeycomb: a template for reproducible psychophysiological tasks for clinic, laboratory, and home use. Revista Brasileira De Psiquiatria (Sao Paulo, Brazil : 1999)
Diekhof EK, Geana A, Ohm F, et al. (2021) The Straw That Broke the Camel's Back: Natural Variations in 17β-Estradiol and COMT-Val158Met Genotype Interact in the Modulation of Model-Free and Model-Based Control. Frontiers in Behavioral Neuroscience. 15: 658769
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